We propose a novel transformer-style architecture called Global-Local Filter Network (GLFNet) for medical image segmentation and demonstrate its state-of-the-art performance. We replace the self-attention mechanism with a combination of global-local filter blocks to optimize model efficiency. The global filters extract features from the whole feature map whereas the local filters are being adaptively created as 4x4 patches of the same feature map and add restricted scale information. In particular, the feature extraction takes place in the frequency domain rather than the commonly used spatial (image) domain to facilitate faster computations. The fusion of information from both spatial and frequency spaces creates an efficient model with regards to complexity, required data and performance. We test GLFNet on three benchmark datasets achieving state-of-the-art performance on all of them while being almost twice as efficient in terms of GFLOP operations.
Multi-dimension Transformer with Attention-based Filtering for Medical Image Segmentation
University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；Chongqing University of Posts and Telecommunications（重庆邮电大学）；Alipurduar Government Engineering and Management College（阿里布尔杜阿尔政府工程与管理学院）
AIF-SFDA: Autonomous Information Filter-driven Source-Free Domain Adaptation for Medical Image Segmentation
Research Institute of Trustworthy Adaptive Systems, Southern University of Science and Technology（南方科技大学可信自适应系统研究院）；Southern University of Science and Technology（南方科技大学）；Beijing Information Science & Technology University（北京信息科技大学）；Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究院）
RaffeSDG: Random Frequency Filtering enabled Single-source Domain Generalization for Medical Image Segmentation
Faculty of Biomedical Engineering, Shenzhen University of Advanced Technology（深圳先进技术大学生物医学工程学院）；The Department of Computer Science and Engineering, Southern University of Science and Technology（南方科技大学计算机科学与工程系）；The Department of Biostatistics, School of Global Public Health, New York University（纽约大学全球公共卫生学院生物统计学系）；Department of Ophthalmology, Peking University People's Hospital（北京大学人民医院眼科部门）；Beijing Key Laboratory of Ocular Disease and Optometry Science, Peking University People’s Hospital（北京大学人民医院眼病与视光学科学重点实验室）
FPL+: Filtered Pseudo Label-based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China（电子科技大学机械与电气工程学院）；Shanghai AI laboratory（上海人工智能实验室）；West China Hospital, Sichuan University（四川大学华西医院）；West China Biomedical Big Data Center, West China Hospital, Sichuan University（四川大学华西医院华西生物医学大数据中心）；SenseTime Research（商汤科技研究院）
Complementary Information Mutual Learning for Multimodality Medical Image Segmentation
School of Computer Science and Technology（计算机科学与技术学院）；East China Normal University（华东师范大学）；School of Data Science（数据科学学院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）；Huashan Hospital（华山医院）；Fudan University（复旦大学）；Shanghai AI Laboratory（上海人工智能实验室）；School of Software Engineering（软件工程学院）；Shanghai Research Institute for Intelligent Autonomous Systems（上海智能自主系统研究院）；Tongji University（同济大学）
Towards Reliable Medical Image Segmentation by Modeling Evidential Calibrated Uncertainty
National Key Laboratory of Fundamental Science on Synthetic Vision, the College of Computer Science, Sichuan University（国家基础科学合成视觉重点实验室、计算机科学学院、四川大学）；Department of Radiology, West China Hospital, Sichuan University（放射科、西昌医院、四川大学）；Saw Swee Hock School of Public Health, National University of Singapore（Saw Swee Hock 公共卫生学院、新加坡国立大学）；Department of Mathematics, Sichuan University（数学系、四川大学）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（高性能计算研究所（IHPC）、科学、技术与研究局（A*STAR））；Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore（创新与精准眼健康中心、 Yong Loo Lin 医学院、新加坡国立大学）；Singapore Eye Research Institute, Singapore National Eye Centre（新加坡眼研究所、新加坡国家眼科中心）
Diffusion-Based Data Augmentation for Medical Image Segmentation
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；Dept. of Engineering for Innovation Medicine, University of Verona（威尼斯大学创新医学工程系）
TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation
Xiamen University（厦门大学）；Beihang University（北京航空航天大学）；University of Hull（赫尔大学）；Chinese Academy of Sciences（中国科学院）
医学图像分割是医学信息分析中最基本的任务之一。到目前为止，已经提出了各种解决方案，包括许多基于深度学习的技术，如U-Net，FC-DenseNet等。然而，由于医学图像中存在固有的放大和失真，以及存在与正常组织密度相似的病变，高精度医学图像分割仍然是一项极具挑战性的任务。在本文中，我们提出了TFCN（Transformers for Fully Convolutional denseNet），通过将ResLinear-Transformer（RL-Transformer）和卷积线性注意块（CLAB）引入FC-DenseNet来解决这个问题。TFCN不仅能够利用CT图像中更多的潜在信息进行特征提取，而且通过CLAB模块可以更有效地捕获和传播语义特征并过滤非语义特征。我们的实验结果表明，TFCN可以达到最先进的性能，在Synapse数据集上的骰子得分为83.72%。此外，我们评估了TFCN对COVID-19公共数据集上病变面积影响的稳健性。Python代码将在https://github.com/HUANGLIZI/TFCNs上公开。
Contrastive Graph Modeling for Cross-Domain Few-Shot Medical Image Segmentation
School of Computer Science and Engineering, Nanjing University of Science and Technology（计算机科学与工程学院，南京理工大学）；University of Chinese Academy of Sciences（中国科学院大学）
Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation
National Engineering Research Center for Multimedia Software（国家多媒体软件工程技术研究中心）；Medical Artificial Intelligence Research Institute of Renmin Hospital, Wuhan University（武汉大学人民医院医学人工智能研究院）；School of Computer Science, Central China Normal University（华中师范大学计算机学院）
Curvilinear object segmentation in medical images based on ODoS filter and deep learning network
School of Electrical and Automation Engineering, East China Jiaotong University（华东交通大学电气与自动化工程学院）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；School of Software, East China Jiaotong University（华东交通大学软件学院）
MedPruner: Training-Free Hierarchical Token Pruning for Efficient 3D Medical Image Understanding in Vision-Language Models
Chinese University of Hong Kong（香港中文大学）；Westlake University（西湖大学）；Southern Medical University（南方医科大学）；Jiangnan University（江南大学）；Dalian University of Technology（大连理工大学）；Shanghai Jiao Tong University（上海交通大学）；University of Copenhagen（哥本哈根大学）
MedDiff-FT: Data-Efficient Diffusion Model Fine-tuning with Structural Guidance for Controllable Medical Image Synthesis
Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Peking University Shenzhen Graduate School South China University of Technology, China
